As mentioned above, the analysis of mesoscopic levels is particularly interesting for studying the dynamics that develop on a network. However, networks themselves are rarely static and very often contacts are made and broken with temporalities that can be very complex. It is therefore easy to understand that it is not possible, for example, to study the spread of an epidemic in a social network without taking into account the temporality of the links in the network. New challenges in network science therefore concern temporal networks or time-varying networks for which each edge is associated with one or more time intervals during which the links between the two nodes exist [1]}, [2]}. Conventional measurements used on static networks can no longer be used as is and must be adapted. At the mesoscopic level, the notion of community and community detection algorithms are defined as a function of time, with communities that change over time [3]}. Several authors have also adapted the notions of Core-Periphery [4]}, [5]}, [6]}.
